Intelligent Prediction Method of Urban Road Traffic Congestion Based on Knowledge Graph Technology

This paper introduces an innovative urban traffic congestion prediction model, which combines knowledge graph and graph neural network technology to improve prediction accuracy and traffic management efficiency. Firstly, the data preprocessing steps are described in detail, including the integration and standardization of traffic, weather and event data. Then, the traffic knowledge graph is designed, the entity, relationship and ontology structure are defined, and the dynamic update mechanism is constructed. The core of the model adopts GNN combined with LSTM, embedding entity relations through TransE, learning spatiotemporal features through graph convolutional network, and capturing time series dynamics through RNN. The experimental design is based on a one-year comprehensive traffic dataset. The results show that the proposed KG-GNN model performs well in prediction accuracy, stability and interpretation, significantly reduces the average congestion time and delay index, and improves public satisfaction. Case studies further demonstrate the effectiveness of the model in easing congestion, optimizing public transport and enhancing travel experience.